Some aspects of the improvement in skill of numerical weather prediction

Recent verification statistics show a considerable improvement in the accuracy of forecasts from three global numerical weather prediction systems. The improvement amounts to about a 1‐day gain in predictability of mean‐sea‐level pressure and 500 hPa height over the last decade in the northern hemisphere, with a similar gain over the last 3 years in the southern hemisphere. Differences between the initial analyses from the three systems have been substantially reduced.

[1]  C E Leith,et al.  Objective Methods for Weather Prediction , 1978 .

[2]  A. Hollingsworth,et al.  The Performance of a Medium-Range Forecast Model in Winter–Impact of Physical Parameterizations , 1980 .

[3]  J. Wallace,et al.  Teleconnections in the Geopotential Height Field during the Northern Hemisphere Winter , 1981 .

[4]  Results of the WMO/CAS NWP data study and intercomparison project for forecasts for the Northern Hemisphere in 1979-80 , 1981 .

[5]  E. Lorenz.  Atmospheric predictability experiments with a large numerical model , 1982 .

[6]  Lennart Bengtsson,et al.  Medium range weather prediction operational experience at ECMWF , 1983 .

[7]  A. Hollingsworth,et al.  The response of numerical weather prediction systems to fgge level iib data. Part I: Analyses , 1985 .

[8]  A. Hollingsworth,et al.  The statistical structure of short-range forecast errors as determined from radiosonde data Part II: The covariance of height and wind errors , 1986 .

[9]  A. Simmons.  Numerical Prediction: Some Results from Operational Forecasting at ECMWF , 1986 .

[10]  A. Dalcher,et al.  Error growth and predictability in operational ECMWF forecasts , 1987 .

[11]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[12]  Peter J. Webster,et al.  Random error growth in NMC's global forecasts , 1994 .

[13]  Philippe Courtier,et al.  Use of cloud‐cleared radiances in three/four‐dimensional variational data assimilation , 1994 .

[14]  Roberto Buizza,et al.  The Singular-Vector Structure of the Atmospheric Global Circulation , 1995 .

[15]  T. Petroliagis,et al.  Error growth and estimates of predictability from the ECMWF forecasting system , 1995 .

[16]  Hannu Savijärvi,et al.  Error Growth in a Large Numerical Forecast System , 1995 .

[17]  A. Simmons,et al.  Implementation of the Semi-Lagrangian Method in a High-Resolution Version of the ECMWF Forecast Model , 1995 .

[18]  Philippe Courtier,et al.  Sensitivity of forecast errors to initial conditions , 1996 .

[19]  Influence of observations on the operational ECMWF system , 1997 .

[20]  Roger Saunders,et al.  Near-Surface Satellite Wind Observations of Hurricanes and Their Impact on ECMWF Model Analyses and Forecasts , 1998 .

[21]  Jean-Noël Thépaut,et al.  The information content of clear sky IASI radiances and their potential for numerical weather prediction , 1998 .

[22]  J. Derber,et al.  A reformulation of the background error covariance in the ECMWF global data assimilation system , 1999 .

[23]  M. Matricardi,et al.  An improved fast radiative transfer model for assimilation of satellite radiance observations , 1999 .

[24]  Roberto Buizza,et al.  Forecast skill of the ECMWF model using targeted observations during FASTEX , 1999 .

[25]  Roger W. Saunders,et al.  Use and Impact of Satellite Atmospheric Motion Winds on ECMWF Analyses and Forecasts , 1999 .

[26]  Heikki Järvinen,et al.  Variational assimilation of time sequences of surface observations with serially correlated errors , 1999 .

[27]  Tim N. Palmer,et al.  Signature of recent climate change in frequencies of natural atmospheric circulation regimes , 1999, Nature.

[28]  John Derber,et al.  The use of TOVS level‐1b radiances in the NCEP SSI analysis system , 2000 .

[29]  N. B. Ingleby,et al.  The Met. Office global three‐dimensional variational data assimilation scheme , 2000 .

[30]  Stephen J. English,et al.  A comparison of the impact of TOVS arid ATOVS satellite sounding data on the accuracy of numerical weather forecasts , 2000 .

[31]  C. Jakob,et al.  Revision of convection, radiation and cloud schemes in the ECMWF integrated forecasting system , 2000 .

[32]  Stephen A. Klein,et al.  A parametrization of the effects of cloud and precipitation overlap for use in general‐circulation models , 2000 .

[33]  A. K. Betts,et al.  O ine validation of the ERA 40 surface scheme , 2000 .

[34]  A major operational forecast model change , 2001 .

[35]  Roger Saunders,et al.  Impact of a New Cloud Motion Wind Product from Meteosat on NWP Analyses and Forecasts , 2001 .

[36]  F. Bouttier,et al.  Observing‐system experiments in the ECMWF 4D‐Var data assimilation system , 2001 .

[37]  Graeme Kelly,et al.  A satellite radiance‐bias correction scheme for data assimilation , 2001 .

[38]  J. Bidlot,et al.  IMPACT AND FEEDBACK OF OCEAN WAVES ON THE ATMOSPHERE by P A , 2001 .

[39]  A. Hollingsworth,et al.  Storm prediction over Europe using the ECMWF Ensemble Prediction System , 2002 .

[40]  A. Hollingsworth,et al.  Diagnosis of systematic forecast errors dependent on flow pattern , 2002 .

[41]  J. Mahfouf,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. II: Experimental results with improved physics , 2022 .